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Estimation of Project Performance Using Fuzzy Linear Regression

퍼지회귀분석을 이용한 프로젝트 성과예측

  • 박영만 (경남대학교 경영학부) ;
  • 박광박 (경남대학교 응용수리학부)
  • Published : 2008.12.25

Abstract

Fuzzy regression model is used in evaluating relationship between the dependent and independent variables. If linguistic data are obtained, ordinary regression have limitation due to oversimplification of data. In this paper, fuzzy regression model with fuzzy input-output data for estimation of project performance is used.

퍼지회귀분석은 독립변수들과 종속변수간의 관계를 평가하는데 사용된다. 만약 언어적 표현으로 된 자료를 처리할 때 일반적인 회귀분석을 사용한다면 과도한 단순화 때문에 어느 정도 한계를 가진다. 본 논문에서는 프로젝트의 성과를 예측하기 위해 퍼지 입출력을 갖는 퍼지회귀분석을 사용한다.

Keywords

References

  1. H. Tanaka, S. Uejima, and K. Asai, 'Linear Regression Analysis with Fuzzy Model', IEEE Trans. Systems, Man and Cybernetics, 12, 1982, 903-907 https://doi.org/10.1109/TSMC.1982.4308925
  2. P. Diamond and R. K. Korner,'Extended Fuzzy Linear Models and Least-Squares Estimates', Journal of Computational and Applied Mathematics, 9, 1997, 15-32
  3. Y. O. Chang and B. M. Ayyub, 'Fuzzy Regression Methods - a Comparative Assessment', Fuzzy Sets and Systems, 119, 2001, 187-203 https://doi.org/10.1016/S0165-0114(99)00091-3
  4. B. Kim and R.R.Bishu, 'Evaluation of Fuzzy Linear Regression Models by Comparing Membership Functions', Fuzzy Sets and Systems, 100, 343-352 https://doi.org/10.1016/S0165-0114(97)00100-0
  5. P. Diamond, 'Correlation-based Feature Selection of Discrete and Numeric Class Machine Learning', Proceedings of the International Conference on Machine Learning, 2000, 359-366
  6. M. S. Yang and T. S. Lin, 'Fuzzy Least-Squares Linear Regression Analysis for Fuzzy Input-Output Data', Fuzzy Sets and Systems, 126, 2002, 389-399 https://doi.org/10.1016/S0165-0114(01)00066-5
  7. A. Celmins, 'Least Square Model Fitting to Fuzzy Vector Data', Fuzzy Sets and Systems, 32, 1987, 245-269
  8. D. Savic and W. Pedrycz, 'Evaluation of Fuzzy Regression Models', Fuzzy Sets and Systems, 39, 1991, 51-63 https://doi.org/10.1016/0165-0114(91)90065-X

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